AI Driven Cybersecurity Threat Detection and Mitigation Workflow

Cybersecurity Threat Detection and Mitigation

1. Threat Identification

1.1 Data Collection

Gather data from various sources such as network logs, user activity, and system alerts.

1.2 AI-Driven Analytics

Utilize AI tools like Darktrace and Vectra AI to analyze the collected data for unusual patterns or anomalies that may indicate a security threat.

2. Threat Assessment

2.1 Risk Evaluation

Assess the potential impact and likelihood of identified threats using AI algorithms to prioritize vulnerabilities.

2.2 Automated Threat Scoring

Implement tools like IBM QRadar that apply machine learning to assign threat scores based on historical data and current threat intelligence.

3. Threat Mitigation

3.1 Incident Response Planning

Develop a response plan that outlines steps to be taken in the event of a detected threat, incorporating AI-driven decision-making tools for rapid response.

3.2 AI-Enhanced Security Solutions

Deploy AI-based security solutions such as CrowdStrike and SentinelOne that provide real-time threat detection and automated response capabilities.

4. Continuous Monitoring

4.1 Real-Time Surveillance

Utilize AI tools for ongoing monitoring of network activity, leveraging platforms like Splunk to identify new threats as they arise.

4.2 Feedback Loop

Establish a feedback mechanism to continuously improve threat detection algorithms based on new data and threat landscapes.

5. Reporting and Compliance

5.1 Incident Reporting

Document detected threats and responses using compliance-oriented tools like RSA Archer to ensure regulatory adherence.

5.2 Performance Metrics

Analyze the effectiveness of the threat detection and mitigation process through key performance indicators (KPIs) to refine strategies.